Effectiveness of Digital Cognitive Behavioral Therapy for Insomnia: A Meta-Analysis of Randomized Controlled Trials

Abouzar  Nazari; Ali  Mirzakhani; Gholamreza  Garmaroudi; Mohsen  Amani

doi:10.18502/ijps.v20i4.19689

Abouzar Nazari Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Ali Mirzakhani Student Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Gholamreza Garmaroudi Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Mohsen Amani Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

DOI: https://doi.org/10.18502/ijps.v20i4.19689

Keywords: Cognitive Behavioral Therapy; Digital Health; Insomnia; Meta-Analysis; Randomized Controlled Trials

Abstract

Objective: This study aimed to evaluate the effectiveness of fully automated Digital Cognitive Behavioral Therapy for Insomnia (dCBT-I) interventions in reducing insomnia severity through a systematic review and meta-analysis of randomized controlled trials (RCTs).

Method: A systematic search was conducted across multiple databases, including PubMed, PsycINFO, Web of Science, Scopus, and Google Scholar, to identify RCTs evaluating fully automated dCBT-I. Eligible studies were included those assessing adults diagnosed with insomnia using validated criteria or scales, utilizing digital delivery platforms, and reporting quantitative insomnia severity outcomes. A meta-analysis was performed using a random-effects model, with standardized mean differences (SMDs) and 95% confidence intervals (CIs) as the primary effect measures. Subgroup and sensitivity analyses were conducted to explore sources of heterogeneity.

Results: A total of 49 RCTs involving 20,118 participants were included. Fully automated dCBT-I significantly reduced insomnia severity compared to control conditions (WMD: -3.42; 95% CI: -4.35 to -2.48; P < 0.001). Subgroup analyses revealed greater effectiveness in studies using rigorous diagnostic criteria, as well as among U.S.-based populations. Despite substantial heterogeneity (I² > 98%), sensitivity analyses confirmed the robustness of findings. Funnel plot asymmetry suggested minor potential publication bias, though Egger’s test did not confirm significant bias (P = 0.494).

Conclusion: Fully automated dCBT-I programs effectively reduce insomnia severity, offering a scalable, accessible solution to overcome barriers in traditional CBT-I delivery. However, variability in study methodologies and the predominance of studies from high-income countries highlight the need for further research. Future directions include incorporating objective sleep measures, assessing long-term outcomes, and adapting interventions to diverse cultural and economic contexts. Fully automated dCBT-I holds transformative potential for addressing insomnia on a global scale.